Model Selection
Let each AI task run on the model that suits its quality, speed, and cost needs.
What it adds
A per-task model choice, drawn from the models the app already has configured, with capability filtering and safe defaults.
What your agent is told to do
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What your agent is told to do
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Enumerate the models the app already has access to and record what each one can actually do: context capacity, whether it can return the structured output the task requires, whether it supports the tools the task calls, and its relative cost and speed.
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Offer only the models that satisfy the task's requirements. A task that needs structured output must not list a model that cannot reliably produce it, because the failure appears later as malformed responses rather than as an unavailable option.
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Store the choice against the specific task, not as one global setting. A single default forces a summarisation task and a classification task onto the same tier when they have opposite needs.
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Present the choice in plain terms — faster, higher quality, lower cost, and where the data is processed — rather than exposing provider naming and parameter detail to users who did not ask for it. Advanced settings can stay behind a disclosure for the people who need them.
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Do not let a removed or deprecated model become a silent failure. Fall back to a compatible configured model, record that the substitution happened, and surface it to an administrator.
Edge cases it handles
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Edge cases it handles
8- The list must be filtered by what the task actually needs, so a model that cannot honour the required output shape or call the required tools is never offered for that task.
- Provider-specific parameters and naming should stay hidden from ordinary users behind a plain description of speed, quality, and cost, while remaining reachable for administrators.
- Choices belong to individual tasks. One global default applied everywhere is either too expensive for the cheap work or too weak for the hard work.
- A model that is removed, deprecated, or newly unavailable must resolve to a compatible substitute with a recorded, visible notice, rather than throwing an error at the next run.
- Where models differ materially in cost or in where data is processed, state that at the point of selection. Discovering it on an invoice or in a compliance review is too late.
- Changing the model for a task must not silently change results already produced. Existing outputs keep their original attribution.
- A selection that would exceed a configured spend ceiling must be blocked or flagged at selection time, not at run time.
- If no configured model satisfies the task, say so explicitly rather than defaulting to one that will fail partway through.
Definition of done
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Definition of done
8- Each AI task stores its own model choice rather than inheriting a single global default.
- Only models capable of the task's output shape and tool requirements are offered for it.
- Cost and data-processing differences are stated at the point of selection.
- A deprecated or removed model resolves to a compatible substitute, with the substitution recorded and surfaced.
- Provider-specific detail is hidden from ordinary users and reachable by administrators.
- Changing a task's model leaves previously produced outputs and their attribution untouched.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
AI Cost Budgets
AI Cost Budgets
Cap what AI features are allowed to spend before the bill arrives.
What it does
Monetary spending limits on AI work, scoped by workspace, feature, and time period, enforced before a run starts.
How it works
- 1 Find every place the app calls a model and route all of them through one accounting point that records estimated and actual spend against a scope. A budget that only covers the chat feature is not a budget.
- 2 Estimate the cost of a run from the size of its input before dispatching it, and refuse anything that would exceed the remaining budget on its own.
- 3 Reserve the estimate against the budget when the run starts, then reconcile to the real usage figures when it finishes, releasing whatever was over-reserved.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-cost-budgets
Multi-Model Routing
Multi-Model Routing
Send each AI request to the right model using rules you can read and test.
What it does
A deterministic routing layer that picks a model per request from task type, context size, latency budget, and data sensitivity.
How it works
- 1 Express routing as explicit, ordered rules over inputs the app can measure: task type, estimated context size, latency budget, and the sensitivity classification of the data involved. A rule set that can be read line by line can be reviewed and tested.
- 2 Make routing deterministic. The same inputs must always produce the same route, so a bad output can be reproduced and a rule change can be evaluated. Randomised or load-based selection turns every incident into guesswork.
- 3 Classify data before routing and refuse to route restricted content to any destination not approved for it. This check is a hard block, not a preference, and it must run before the request is assembled.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/multi-model-routing
Webhook Endpoint Management
Webhook Endpoint Management
Let users choose where the app sends events, and which events go there.
What it does
Creating, editing, and testing outbound webhook endpoints, with destination validation and per-endpoint event subscriptions.
How it works
- 1 Let users register endpoints with a URL and a chosen set of event types. Default the subscription to nothing — an endpoint that silently receives every event is a data leak waiting to happen.
- 2 Require HTTPS and validate the destination before saving: resolve the host and refuse private, loopback, link-local, and metadata-service addresses.
- 3 Re-resolve and re-check the destination at delivery time, not only at save time. DNS can be repointed at an internal address after validation passes.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/webhook-endpoint-management
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.